From 998a705b341a9483174a4418f5f7cd62e7e204ab Mon Sep 17 00:00:00 2001
From: Xcreate672 <3534270192@qq.com>
Date: Sat, 6 Jun 2026 15:51:33 +0800
Subject: [PATCH 1/3] =?UTF-8?q?=E6=94=B9=E6=88=90=E5=AF=B9=E8=B1=A1?=
 =?UTF-8?q?=E6=A0=BC=E5=BC=8F=20Update=20ignore=5Fusers.json?=
MIME-Version: 1.0
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改成对象格式
---
 ignore_users.json | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/ignore_users.json b/ignore_users.json
index ed695fe839..584595373e 100644
--- a/ignore_users.json
+++ b/ignore_users.json
@@ -1,5 +1,8 @@
 [
-    "Haidong Wang",
-    "donghaiwang",
-    "whd@hutb.edu.cn"
-]
\ No newline at end of file
+    {
+        "name": "Haidong Wang",
+        "github": "donghaiwang",
+        "email": "whd@hutb.edu.cn",
+        "role": "author"
+    }
+]

From 97797c0c66da04312ff80b24a22c7732fdd6253e Mon Sep 17 00:00:00 2001
From: Xcreate672 <3534270192@qq.com>
Date: Tue, 9 Jun 2026 15:10:59 +0800
Subject: [PATCH 2/3] =?UTF-8?q?Update=20gaussian=5Fmixture.md=E8=A1=A5?=
 =?UTF-8?q?=E5=85=85=E9=AB=98=E6=96=AF=E6=B7=B7=E5=90=88=E6=A8=A1=E5=9E=8B?=
 =?UTF-8?q?=E5=85=AC=E5=BC=8F=E4=B8=8E=E4=BB=A3=E7=A0=81=E7=A4=BA=E4=BE=8B?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

补充高斯混合模型公式与代码示例
---
 docs/gaussian_mixture.md | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/docs/gaussian_mixture.md b/docs/gaussian_mixture.md
index 4f2ea7cbce..5bc0e45fb0 100644
--- a/docs/gaussian_mixture.md
+++ b/docs/gaussian_mixture.md
@@ -44,3 +44,34 @@
 - 异常检测
 - 图像分割
 - 语音识别
+- 
+## 数学公式
+
+GMM 的概率密度函数为：
+
+`p(x) = Σ π_k * N(x | μ_k, Σ_k)`
+
+其中：
+- K 为高斯成分数量
+- π_k 为第 k 个成分的混合权重，满足 Σπ_k = 1
+- N(x | μ_k, Σ_k) 为第 k 个高斯分布
+
+## 代码示例
+
+使用 scikit-learn 拟合高斯混合模型：
+
+```python
+from sklearn.mixture import GaussianMixture
+import numpy as np
+
+# 生成示例数据
+X = np.random.randn(300, 2)
+
+# 创建并训练模型
+gmm = GaussianMixture(n_components=3, random_state=0)
+gmm.fit(X)
+
+# 预测类别
+labels = gmm.predict(X)
+print("各成分权重:", gmm.weights_)
+```

From 198c95dcba95f522f96a27f2a3e46670f46fbf99 Mon Sep 17 00:00:00 2001
From: Xcreate672 <3534270192@qq.com>
Date: Wed, 10 Jun 2026 17:18:18 +0800
Subject: [PATCH 3/3] =?UTF-8?q?=20=E8=A1=A5=E5=85=85=E5=8F=97=E9=99=90?=
 =?UTF-8?q?=E7=8E=BB=E5=B0=94=E5=85=B9=E6=9B=BC=E6=9C=BAPyTorch=E4=BB=A3?=
 =?UTF-8?q?=E7=A0=81=E7=A4=BA=E4=BE=8B=20Update=20RBM.md?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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 补充受限玻尔兹曼机PyTorch代码示例
---
 docs/RBM.md | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)

diff --git a/docs/RBM.md b/docs/RBM.md
index e9e7deb364..722ea270b8 100644
--- a/docs/RBM.md
+++ b/docs/RBM.md
@@ -42,3 +42,40 @@
 - 推荐系统
 - 协同过滤
 - 深度信念网络的基础组件
+- 
+## 代码示例
+
+使用 PyTorch 实现简单的 RBM：
+
+```python
+import torch
+import torch.nn as nn
+
+class RBM(nn.Module):
+    """受限玻尔兹曼机"""
+    def __init__(self, visible_dim, hidden_dim):
+        super(RBM, self).__init__()
+        self.W = nn.Parameter(torch.randn(hidden_dim, visible_dim) * 0.01)
+        self.b_v = nn.Parameter(torch.zeros(visible_dim))
+        self.b_h = nn.Parameter(torch.zeros(hidden_dim))
+
+    def sample_hidden(self, v):
+        """从可见层采样隐藏层"""
+        p_h = torch.sigmoid(torch.matmul(v, self.W.t()) + self.b_h)
+        return p_h, torch.bernoulli(p_h)
+
+    def sample_visible(self, h):
+        """从隐藏层采样可见层"""
+        p_v = torch.sigmoid(torch.matmul(h, self.W) + self.b_v)
+        return p_v, torch.bernoulli(p_v)
+```
+
+## 运行环境
+
+- Python 3.7+
+- PyTorch 1.8+
+
+## 参考资料
+
+- [PyTorch 官方文档](https://pytorch.org/docs/stable/index.html)
+- [深度信念网络介绍](https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf)